We build on the recent insights from [1]}, [2]}, [3]} which compare the importance of example replay against other more complex solutions. Specifically, Prabhu [1]} retrospects the progress made by the complex continual learning methodologies and show that a greedy exemplar selection strategy for replay in incremental learning consistently outperforms the state-of-the-art methods by a large margin. Knoblauch [2]} develops a theoretical justification for the unwarranted power of replay methods. They prove that an optimal continual learner solves an NP-hard problem and requires infinite memory. The effectiveness of storing few examples and replaying has been found effective in the related few-shot object detection setting by Wang [3]}. These motivates us to use a relatively simple methodology for ORE to mitigate forgetting i.e., we store a balanced set of exemplars and finetune the model after each incremental step on these. At each point, we ensure that a minimum of \(N_{ex}\)  instances for each class are present in the exemplar set.
